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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

AI ensemble for signal detection of higher order gravitational wave modes of quasi-circular, spinning, non-precessing binary black hole mergers

We introduce spatiotemporal-graph models that concurrently process data from the twin advanced LIGO detectors and the advanced Virgo detector. We trained these AI classifiers with 2.4 million IMRPhenomXPHM waveforms that describe quasi-circular, spinning, non-precessing binary black hole mergers with component masses m{1,2}∈[3M⊙,50M⊙], and individual spins sz{1,2}∈[−0.9,0.9]; and which include the (ℓ,|m|)={(2,2),(2,1),(3,3),(3,2),(4,4)} modes, and mode mixing effects in the ℓ=3,|m|=2 harmonics. We trained these AI classifiers within 22 hours using distributed training over 96 NVIDIA V100 GPUs in the Summit supercomputer. We then used transfer learning to create AI predictors that estimate the total mass of potential binary black holes identified by all AI classifiers in the ensemble. We used this ensemble, 3 classifiers for signal detection and 2 total mass predictors, to process a year-long test set in which we injected 300,000 signals. This year-long test set was processed within 5.19 minutes using 1024 NVIDIA A100 GPUs in the Polaris supercomputer (for AI inference) and 128 CPU nodes in the ThetaKNL supercomputer (for post-processing of noise triggers), housed at the Argonne Leadership Computing Facility. These studies indicate that our AI ensemble provides state-of-the-art signal detection accuracy, and reports 2 misclassifications for every year of searched data. This is the first AI ensemble designed to search for and find higher order gravitational wave mode signals.

Tian, Minyang↗
DOE OSTI2024

Climate change-resilient snowpack estimation in the Western United States

Abstract In the 21st century, warmer temperatures and changing atmospheric circulation will likely produce unprecedented changes in Western United States snowfall 1–3 , with impacts on the timing, amount, and spatial patterns of snowpack 4–7 . The ~900 snow pillow stations are indispensable to water resource management by measuring snow-water equivalent (SWE) 8,9 in strategic but fixed locations 10,11 . However, this network may not be impacted by climate change in the same way as the surrounding area 12 and thus fail to accurately represent unmeasured locations; climate change thereby threatens our ability to measure the effects of climate change on snow. In this work, we show that maintaining the current peak SWE estimation skill is nonetheless possible. We find that explicitly including spatial correlations—either from gridded observations or learned by the model—improves skill at predicting distributed snowpack from sparse observations by 184%. Existing artificial intelligence methods can be useful tools to harness the many available sources of snowpack information to estimate snowpack in a nonstationary climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluating proxy influence in assimilated paleoclimate reconstructions – Testing the exchangeability of two ensembles of spatial processes

Climate field reconstructions (CFR) attempt to estimate spatiotemporal fields of climate variables in the past using climate proxies such as tree rings, ice cores, and corals. Data Assimilation (DA) methods are a recent and promising new means of deriving CFRs that optimally fuse climate proxies with climate model output. Despite the growing application of DA-based CFRs, little is understood about how much the assimilated proxies change the statistical properties of the climate model data. To address this question, we propose a robust and computationally efficient method, based on functional data depth, to evaluate differences in the distributions of two spatiotemporal processes. In this study, we apply our test to study global and regional proxy influence in DA-based CFRs by comparing the background and analysis states, which are treated as two samples of spatiotemporal fields. We find that the analysis states are significantly altered from the climate-model-based background states due to the assimilation of proxies. Moreover, the difference between the analysis and background states increases with the number of proxies, even in regions far beyond proxy collection sites. Our approach allows us to characterize the added value of proxies, indicating where and when the analysis states are distinct from the background states.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Measuring and Valuing Resilience: A Literature Review for the Power Sector

The New York State Energy Research and Development Authority (NYSERDA) partnered with NREL to address some of the key research needs for New York State in advancing power sector resilience. NYSERDA requested a literature review specifically focused on measuring and valuing energy resilience to identify existing work and gaps in research to inform NYSERDA's energy resilience efforts. While the costs of resilience investments are relatively well known, it is also important to adequately convey the benefits of these investments. This often means assigning metrics and estimating the dollar value of potential advantages of resilience enhancements.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Bridging the Gap on Data, Metrics, and Analyses for Grid Resilience to Weather Events: Information that utilities can provide regulators, state energy offices, and other stakeholders

A growing number of states require regulated utilities to file resilience plans to improve the electric grid’s ability to anticipate, withstand, adapt to and recover from increasingly severe weather events. This report aims to help state regulators identify and request data, metrics, and analyses from utilities and use it in decisions on utility resilience plans and investments. The report reviews state requirements and utility plans focused on overall grid resilience, climate change resilience and vulnerabilities, infrastructure modernization, storm protection, and wildfire mitigation. It details types of data, metrics, and analyses across five categories--and provides examples of each from the utility plans. The first category is vulnerability assessments, or evaluations of the susceptibility of systems, communities, or assets to potential harm from identified hazards. The second is data on hazards and the exposure of utility assets and customers to these hazards. The third is attribute metrics, or system characteristics that contribute to or describe the resilience of a system. The fourth is performance metrics, which are impacts of resilience investments on system performance--typically a reduction of negative impacts from hazard events. Finally, evaluation and prioritization are analyses that utilities conduct to estimate impacts from resilience measures (evaluation) and prioritize measures based on costs and estimated impacts (prioritization). The report concludes with examples of key trends and emerging best practices for states and utilities, and identifies areas for further research.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Determination of Stress-Free Lattice Spacing (D₀) for Residual Stress Relaxation Measurement in Ni-based Superalloys by Neutron Diffraction

The residual stress and its relaxation in critical engineering components are key to structural materials reliability. The determination of residual stress in bulk engineering structures like Ni-based superalloys can be conducted by measuring the lattice strain with penetrating neutron diffraction. The state-of-the-art engineering diffraction beamline VULCAN at the Oak Ridge National Laboratory (ORNL) Spallation Neutron Source (SNS) provides the critical capability to evaluate the residual strain/stress relaxation in engineering component thanks to the high flux and event data features. By the means of residual stress/strain calculation, it is critical to accurately determine the stress-free lattice spacing d₀, which can be altered by the change of chemistry, unrelieved stress, and measurement scheme. Here we reported d₀ determination for determining residual stress relaxation and distribution in Alloy 718 superalloys after different quenching treatments. Selection of locations for d₀ measurement was discussed by considering the neutron path, attenuation and sample alignment. A dynamic d₀ that resulted from atom diffusion and chemistry change was estimated as function of time for the in-situ relaxation characterization. It demonstrated how the dynamic d₀ values may influence on the strain calculation in different thermally treated samples. The results highlight the importance of dynamic d₀ for in-situ relaxation involving chemical changes and provide guidance to the dynamic d₀ measurement and simulation.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Online PMU Missing Value Replacement Via Event-Participation Decomposition

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. Here, the method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗
DOE OSTI2020

On the Solution of ℓ 0 -Constrained Sparse Inverse Covariance Estimation Problems

The sparse inverse covariance matrix is used to model conditional dependencies between variables in a graphical model to fit a multivariate Gaussian distribution. Estimating the matrix from data are well known to be computationally expensive for large-scale problems. Sparsity is employed to handle noise in the data and to promote interpretability of a learning model. Although the use of a convex ℓ 1 regularizer to encourage sparsity is common practice, the combinatorial ℓ 0 penalty often has more favorable statistical properties. In this paper, we directly constrain sparsity by specifying a maximally allowable number of nonzeros, in other words, by imposing an ℓ 0 constraint. Here, we introduce an efficient approximate Newton algorithm using warm starts for solving the nonconvex ℓ 0 -constrained inverse covariance learning problem. Numerical experiments on standard data sets show that the performance of the proposed algorithm is competitive with state-of-the-art methods.

$\ell_0$-Constrained↗
DOE OSTI2026

Active operator learning with predictive uncertainty quantification for partial differential equations

With the increased prevalence of neural operators being used to provide rapid solutions to partial differential equations (PDEs), understanding the accuracy of model predictions and the associated error levels is necessary for deploying reliable surrogate models in scientific applications. Existing uncertainty quantification (UQ) frameworks employ ensembles or Bayesian methods, which can incur substantial computational costs during both training and inference. Here, we propose a lightweight predictive UQ method tailored for Deep operator networks (DeepONets) that also generalizes to other operator networks. Numerical experiments on linear and nonlinear PDEs demonstrate that the framework’s uncertainty estimates are unbiased and provide accurate out-of-distribution uncertainty predictions with a sufficiently large training dataset. Our framework provides fast inference and uncertainty estimates that can efficiently drive outer-loop analyses that would be prohibitively expensive with conventional solvers. We demonstrate how predictive uncertainties can be used in the context of Bayesian optimization and active learning problems to yield improvements in accuracy and data-efficiency for outer-loop optimization procedures. In the active learning setup, we extend the framework to Fourier Neural Operators (FNO) and describe a generalized method for other operator networks. To enable real-time deployment, we introduce an inference strategy based on precomputed trunk outputs and a sparse placement matrix, reducing evaluation time by more than a factor of five. Our method provides a practical route to uncertainty-aware operator learning in time-sensitive settings.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗
DOE OSTI2025

An update to the Sandia method for creating Typical Meteorological Years from a limited pool of calendar years

Typical Meteorological Years (TMYs) are essential for the efficient evaluation of energy system performance. Ideally, 30 years of weather data are required to generate TMYs, but significantly fewer years are typically available due to practical limitations. To address this issue, an update to the Sandia method was developed, referred to as the Argonne method, to create TMYs from a limited number of years. Furthermore, this method enhances candidate diversity by systematically shifting original candidate months forward or backward by specific days, creating an expanded pool of candidates. The effectiveness of the Argonne method was validated through statistical testing, comparison of monthly average weather parameters, and numerical simulations. The results demonstrate a high probability of identifying at least one shifted month whose cumulative distribution functions of weather parameters closely align with long-term distributions. In 67 % of all comparisons, the monthly average weather parameters in TMYs generated using the Argonne method exhibit better agreement with long-term averages than TMY3. Moreover, in 74 % of the 318 building simulation cases, the Argonne method outperforms TMY3 in estimating long-term average building heating and cooling demands. Therefore, the Argonne method effectively diversifies the candidate pool and produces typical years that provide more accurate estimations of long-term averages compared to TMY3 when only a limited pool of calendar years (10 years or fewer) is available.

Building energy modeling↗
DOE OSTI2023

Forest reorganisation effects on fuel moisture content can exceed changes due to climate warming in wet temperate forests

Abstract The distributions of vegetation and fire activity are changing rapidly in response to climate warming. In many regions, climate effects on dead fuel moisture content (FMC) are expected to increase future wildfire activity. However, forest FMC is largely driven by microclimate conditions, which are moderated from open weather by vegetation canopies. As shifts in vegetation increase under climate warming, the extent to which future fire activity will be driven by climate directly or associated vegetation shifts remains unresolved. Here, we present a study aimed at quantifying the relative magnitudes of (i) direct climate warming, and (ii) vegetation change, on FMC. Field sites to evaluate these effects were established in a natural laboratory of altered forest states to mature wet temperate forest in south‐eastern Australia. FMC was estimated using a process‐based model and 48 years of reconstructed climate data. Canopy effects on microclimate were captured by transferring inputs from climate to microclimate using models parameterised with field observations. To evaluate the relative magnitude of climate and vegetation effects, we calculated the maximum difference in mean annual FMC across annual climate replicates and compared this to FMC differences across reorganising forest sites. Our results show vegetation effects on FMC can exceed those related to expected climate change. Changes to forest structure and composition increased (+15.7%) and decreased (−12.3%) mean annual FMC, with a larger negative effect when forest cover was completely removed (−18.5%). In contrast, the largest climate effect on FMC was −6.6% across 48‐years of data. Our study demonstrates that the magnitude of vegetation effects on FMC can exceed expected climate change effects. Models of future fire activity that do not account for changing vegetation effects on microclimate are omitting a key biophysical control on FMC and therefore may not be accurately predicting future fire activity.

Brown, Tegan P.↗
DOE OSTI2021

Modeling and Impact of Hyperloop Technology on the Electricity Grid

This paper provides an overview of hyperloop technologies, including a brief discussion of key components of a hyperloop system that determine the electric power requirements as a function of time. At-scale hyperloop systems do not yet exist, so a model was used to generate a set of load profiles for conceptual hyperloop realizations at four locations in the United States: two systems in California and one each in Colorado and Ohio. In all four cases, the modeled hyperloop load profiles included pulses in both active and reactive power. Grid modeling was performed to estimate the grid impacts and for dis-cussing grid integration challenges. The paper discusses how energy storage systems might be used to eliminate the pulsating load characteristics or significantly reduce it to accommodate current grid planning guidelines

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

EAGLE-I County Customer Dataset Fall 2025

This dataset provides a combination of modeled and collected county-level electric customer counts derived from 2023 EIA-861 utility customer data, 2021 HIFLD electric retail service territory boundaries, 2021 LandScan population estimates, and 2025 EAGLE-I customer outages. The dataset details county FIPS code, number of customers, and customer type (modeled, collected, mixed). Outage data in included for all 50 U.S. states, Puerto Rico, and the District of Columbia (excluding other U.S. territories).

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

New U.S. Data Tools are Playing a Crucial Role in Decarbonizing Buildings at Speed, Scale, and Low Cost

Preparing buildings for retrofits traditionally requires expensive on-site audits or timeintensive simulation models. As a result, the majority of buildings fail to pursue cost-saving retrofits. To address these barriers, the U.S. Department of Energy (DOE) has introduced the Building Efficiency Targeting Tool for Energy Retrofits (BETTER)-a new, free, on-line tool that utilizes a data-driven analytical engine and user-friendly web interface to automatically analyze a building's monthly energy usage in response to weather conditions. The tool benchmarks a building's electric and fossil energy usage against peers; estimates energy, cost, and emissions reductions at the building and portfolio levels; recommends energy efficiency measures; and prioritizes buildings for net-zero energy retrofits. Thanks to interoperability with the DOE's Standard Energy Efficiency Data (SEED) platform, BETTER is supporting U.S. jurisdictions to prepare buildings for retrofit at speed, scale, and low cost to comply with energy policies. This paper discusses the use of BETTER and SEED by one of the branches of the California state government to streamline a retrofit program across 455 public non-residential buildings to align with state goals to reduce greenhouse gas emissions. It describes the organization's challenge to reduce energy consumption across a geographically diverse, aging portfolio; explores how BETTER and SEED improved workflow efficiency; presents preliminary results, including avoiding audit costs of $3.28 million and developing the groundwork for retrofit projects estimated to prevent emission of 2,271 t CO2e annually; and provides guidance for other jurisdictions seeking similar results.

BETTER↗
DOE OSTI2022

A multi-criteria land suitability assessment of field allocation decisions for switchgrass

With the goal of evaluating biomass resource availability in certain regions in the United States, the Department of Energy, Department of Agriculture, Environmental Protection Agency, national laboratories, and Forest Service research laboratories, together with academic and industry collaborators, performed a resource analysis study, 2016 Billion-Ton Report: Advancing Domestic Resources for a Thriving Bioeconomy, Volume 1: Economic Availability of Feedstocks, using POLYSYS model to estimate biomass potential. The study provided county- and state-level biomass potential projections, with biomass feedstock production estimates and suitability assessment of lands acreage likely to transition to energy crops. For this analysis, we estimate biomass supply by year 2028, more specifically, lands acreage transition to switchgrass in the Midwest of the United States, across 50 counties in 3 states (Nebraska, Kansas, and Colorado). We propose a multi-criteria site suitability framework, to determine the potential acreage and production for switchgrass, at the field level. Site selection criteria span agronomic and ecosystem services such as previous land cover, crop rotation, soil water holding capacity, crop productivity, field efficiency, topography, soil organic matter, soil leaching propensity, distance to surface water bodies, and soil erosivity. Efficient agricultural field selection for dedicated energy crops field allocation is critical to evaluate biomass resource availability, and it is our position that estimation at the field level would provide a more accurate and useful estimation to support an emerging bioeconomy. Using field suitability scores and county-level assessments, we model land allocation decision for energy crop in the future, estimating the spatial distribution of dynamics of energy crop adoption with priority to fields scoring the highest. Additionally, compared with POLYSYS results from the report mentioned above, our method shows good performance overall, with about 2% and 1.2% of difference in switchgrass acreage allocation and crop production, respectively.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Film Cooling Performance Prediction for Air and Supercritical CO 2

The current study explores the possibility of cooling the vanes and blades of a direct-fired sCO 2 turbine using film cooling. The operating conditions of a direct-fired sCO 2 cycle and thermophysical properties of the fluid at those conditions can alter the flow field characteristics of the coolant jet and its mixing with the mainstream. Very little information is present in the literature regarding the performance of film cooling geometries employing supercritical CO 2 . The objective of this study is to estimate the resulting film cooling effectiveness while also capturing the effects of the crossflow-to-mainstream velocity ratio on the coolant jet. Herein, a computational fluid dynamic model is used to study the coolant jet exiting a cylindrical hole located on a flat plate, with the coolant fed by an internal channel. Steady-state Reynolds-averaged Navier–Stokes equations were solved along with the (shear-stress transport) SST k–ω model to provide the turbulence closure. The operating conditions for the direct-fired sCO 2 turbine are obtained using an in-house Cooled Turbine Model. Numerical predictions revealed that the crossflow effects and jet lift-off were more pronounced in the case of sCO 2 when compared to air. Spatial distribution of flow field and cooling effectiveness are presented at different operating conditions.

30 DIRECT ENERGY CONVERSION↗